{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Koopman eigenfunctions on Slow manifold\n",
    "\n",
    "The linearity, and thus the model performance, of a Koopman model can be analyzed by\n",
    "comparing the linearity of the eigenfunctions evaluated on a test trajectory and the\n",
    "prediction using the corresponding eigenvalue. This will be demonstrated in this\n",
    "tutorial for a two-dimensional system evolving on a slow manifold.\n",
    "\n",
    "For detailed information we refer to\n",
    "\n",
    "[1] Kaiser, Kutz & Brunton, _\"Data-driven discovery of Koopman eigenfunctions for\n",
    "control\"_, Machine Learning: Science and Technology, Vol. 2(3), 035023, 2021,\n",
    "[2] Korda & Mezic, _\"Optimal construction of Koopman eigenfunctions for prediction\n",
    "and control\"_, IEEE Transactions on Automatic Control, Vol. 65(12), 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../src')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pykoopman as pk\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "np.random.seed(42)  # for reproducibility\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from pykoopman.common import slow_manifold\n",
    "nonlinear_sys = slow_manifold(mu=-0.1, la=-1.0, dt=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'we can handle trajectory data with varying-length sequence!')"
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_int = 1\n",
    "n_pts = 16\n",
    "xmin = ymin = -2\n",
    "xmax = ymax = +2\n",
    "xx, yy = np.meshgrid(np.linspace(xmin, xmax, n_pts), np.linspace(ymin, ymax, n_pts))\n",
    "Xdat = np.vstack((xx.flatten(), yy.flatten()))\n",
    "\n",
    "max_n_int = 20\n",
    "\n",
    "traj_list = []\n",
    "for i in range(Xdat.shape[1]):\n",
    "    X, Y = nonlinear_sys.collect_data_discrete(Xdat[:,[i]],\n",
    "                                               np.random.randint(1, max_n_int))\n",
    "    tmp = np.hstack([X, Y[:,-1:]]).T\n",
    "    traj_list.append(tmp)\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.bar(np.arange(len(traj_list)), [len(x) for x in traj_list])\n",
    "plt.xlabel('index of trajectory')\n",
    "plt.ylabel('number of points in each sequence')\n",
    "plt.title('we can handle trajectory data with varying-length sequence!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1cb819728f0>"
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.array([2, -4])  #np.array([2, -4])\n",
    "T = 20\n",
    "t = np.arange(0, T, nonlinear_sys.dt)\n",
    "Xtest = nonlinear_sys.simulate(x0[:, np.newaxis], len(t)-1).T\n",
    "Xtest = np.vstack([x0[np.newaxis, :], Xtest])\n",
    "fig, axs = plt.subplots(2, 1, sharex=True, tight_layout=True, figsize=(12, 6))\n",
    "axs[0].plot(t, Xtest[:, 0], '-', color='blue', label='True')\n",
    "axs[0].set(ylabel=r'$x_1$')\n",
    "axs[1].plot(t, Xtest[:, 1], '-', color='blue', label='True')\n",
    "axs[1].set(ylabel=r'$x_2$', xlabel=r'$t$')\n",
    "axs[1].legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1cb819bfac0>"
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "for X in traj_list:\n",
    "    # for i in range(X.shape[-1]):\n",
    "    # plt.plot([X[0,i], Y[0,i]], [X[1,i], Y[1,i]],'-k',lw=0.7,label=None)\n",
    "    plt.plot(X[:,0], X[:,1],'-k', lw=0.7, label=None,alpha=0.1)\n",
    "\n",
    "# plot test trajectory\n",
    "plt.plot(Xtest[:, 0], Xtest[:, 1],'-r',label='test')\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.title(f\"number of training data = {X.shape[-1]} \")\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try EDMD with polynomial features on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "outputs": [],
   "source": [
    "X_edmd = []\n",
    "Y_edmd = []\n",
    "for i in range(len(traj_list)):\n",
    "    X_edmd.append(traj_list[i][:-1])\n",
    "    Y_edmd.append(traj_list[i][1:])\n",
    "\n",
    "X_edmd = np.vstack(X_edmd)\n",
    "Y_edmd = np.vstack(Y_edmd)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Koopman(observables=Polynomial(degree=3), regressor=EDMD())",
      "text/html": "<style>#sk-container-id-29 {color: black;background-color: white;}#sk-container-id-29 pre{padding: 0;}#sk-container-id-29 div.sk-toggleable {background-color: white;}#sk-container-id-29 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-29 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-29 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-29 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-29 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-29 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-29 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-29 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-29 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-29 div.sk-item {position: relative;z-index: 1;}#sk-container-id-29 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-29 div.sk-item::before, #sk-container-id-29 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-29 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-29 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-29 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-29 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-29 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-29 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-29 div.sk-label-container {text-align: center;}#sk-container-id-29 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-29 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-29\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Koopman(observables=Polynomial(degree=3), regressor=EDMD())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Koopman</label><div class=\"sk-toggleable__content\"><pre>Koopman(observables=Polynomial(degree=3), regressor=EDMD())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">observables: Polynomial</label><div class=\"sk-toggleable__content\"><pre>Polynomial(degree=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Polynomial</label><div class=\"sk-toggleable__content\"><pre>Polynomial(degree=3)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">regressor: EDMD</label><div class=\"sk-toggleable__content\"><pre>EDMD()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">EDMD</label><div class=\"sk-toggleable__content\"><pre>EDMD()</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = pk.regression.EDMD()\n",
    "obsv = pk.observables.Polynomial(degree=3)\n",
    "model_edmd = pk.Koopman(observables=obsv, regressor=regr)\n",
    "model_edmd.fit(X_edmd, y=Y_edmd, dt=nonlinear_sys.dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "koopman eigenvalues = [-1.88130165e+00+0.67548263j -1.88130165e+00-0.67548263j\n",
      " -1.65811154e+00+0.j         -3.77475828e-14+0.j\n",
      " -9.99991625e-02+0.j         -1.99998325e-01+0.j\n",
      " -2.99997487e-01+0.j         -1.09912337e+00+0.j\n",
      " -9.99124206e-01+0.j         -6.82569546e-01+0.j        ]\n"
     ]
    }
   ],
   "source": [
    "koop_eigenvals = np.log(np.linalg.eig(model_edmd.A)[0])/nonlinear_sys.dt\n",
    "\n",
    "print(f\"koopman eigenvalues = {koop_eigenvals}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try NNDMD on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: GPU available: True (cuda), used: True\n",
      "[rank_zero.py:64 -                _info() ] GPU available: True (cuda), used: True\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "[rank_zero.py:64 -                _info() ] TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "[rank_zero.py:64 -                _info() ] IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "[rank_zero.py:64 -                _info() ] HPU available: False, using: 0 HPUs\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\trainer\\configuration_validator.py:69: UserWarning: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\n",
      "  rank_zero_warn(\"You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\")\n",
      "D:\\work\\pykoopman\\src\\pykoopman\\regression\\_nndmd.py:898: UserWarning: Warning: no validation data prepared\n",
      "  warn(\"Warning: no validation data prepared\")\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "[cuda.py:58 -     set_nvidia_flags() ] LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO: \n",
      "  | Name                | Type                    | Params\n",
      "----------------------------------------------------------------\n",
      "0 | _encoder            | FFNN                    | 2.3 K \n",
      "1 | _decoder            | FFNN                    | 6     \n",
      "2 | _koopman_propagator | StandardKoopmanOperator | 9     \n",
      "3 | masked_loss_metric  | MaskedMSELoss           | 0     \n",
      "----------------------------------------------------------------\n",
      "2.3 K     Trainable params\n",
      "0         Non-trainable params\n",
      "2.3 K     Total params\n",
      "0.009     Total estimated model params size (MB)\n",
      "[model_summary.py:83 -            summarize() ] \n",
      "  | Name                | Type                    | Params\n",
      "----------------------------------------------------------------\n",
      "0 | _encoder            | FFNN                    | 2.3 K \n",
      "1 | _decoder            | FFNN                    | 6     \n",
      "2 | _koopman_propagator | StandardKoopmanOperator | 9     \n",
      "3 | masked_loss_metric  | MaskedMSELoss           | 0     \n",
      "----------------------------------------------------------------\n",
      "2.3 K     Trainable params\n",
      "0         Non-trainable params\n",
      "2.3 K     Total params\n",
      "0.009     Total estimated model params size (MB)\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 20 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\loops\\fit_loop.py:281: PossibleUserWarning: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "Training: 0it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ff5fc36f68ec41478a63286dfd2d71c5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "[rank_zero.py:64 -                _info() ] `Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={'activations': 'linear',\n                                        'hidden_sizes': [], 'input_size': 3,\n                                        'output_size': 2},\n                        config_encoder={'activations': 'swish',\n                                        'hidden_sizes': [32, 32, 32],\n                                        'input_size': 2, 'output_size': 3},\n                        dt=0.1, lbfgs=True, look_forward=20,\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={'max_epochs': 10}))",
      "text/html": "<style>#sk-container-id-30 {color: black;background-color: white;}#sk-container-id-30 pre{padding: 0;}#sk-container-id-30 div.sk-toggleable {background-color: white;}#sk-container-id-30 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-30 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-30 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-30 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-30 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-30 div.sk-item {position: relative;z-index: 1;}#sk-container-id-30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-30 div.sk-item::before, #sk-container-id-30 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-30 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-30 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-30 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-30 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-30 div.sk-label-container {text-align: center;}#sk-container-id-30 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-30 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [], &#x27;input_size&#x27;: 3,\n                                        &#x27;output_size&#x27;: 2},\n                        config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                                        &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n                        dt=0.1, lbfgs=True, look_forward=20,\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={&#x27;max_epochs&#x27;: 10}))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-146\" type=\"checkbox\" ><label for=\"sk-estimator-id-146\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Koopman</label><div class=\"sk-toggleable__content\"><pre>Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [], &#x27;input_size&#x27;: 3,\n                                        &#x27;output_size&#x27;: 2},\n                        config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                                        &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n                        dt=0.1, lbfgs=True, look_forward=20,\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={&#x27;max_epochs&#x27;: 10}))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-147\" type=\"checkbox\" ><label for=\"sk-estimator-id-147\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">observables: Identity</label><div class=\"sk-toggleable__content\"><pre>Identity()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Identity</label><div class=\"sk-toggleable__content\"><pre>Identity()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-149\" type=\"checkbox\" ><label for=\"sk-estimator-id-149\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">regressor: NNDMD</label><div class=\"sk-toggleable__content\"><pre>NNDMD(batch_size=1000,\n      config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;, &#x27;hidden_sizes&#x27;: [],\n                      &#x27;input_size&#x27;: 3, &#x27;output_size&#x27;: 2},\n      config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;, &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                      &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n      dt=0.1, lbfgs=True, look_forward=20, normalize_std_factor=1.0,\n      trainer_kwargs={&#x27;max_epochs&#x27;: 10})</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-150\" type=\"checkbox\" ><label for=\"sk-estimator-id-150\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NNDMD</label><div class=\"sk-toggleable__content\"><pre>NNDMD(batch_size=1000,\n      config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;, &#x27;hidden_sizes&#x27;: [],\n                      &#x27;input_size&#x27;: 3, &#x27;output_size&#x27;: 2},\n      config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;, &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                      &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n      dt=0.1, lbfgs=True, look_forward=20, normalize_std_factor=1.0,\n      trainer_kwargs={&#x27;max_epochs&#x27;: 10})</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_forward = 20\n",
    "dlk_regressor = pk.regression.NNDMD(dt=nonlinear_sys.dt, look_forward=look_forward,\n",
    "                                    config_encoder=dict(input_size=2,\n",
    "                                                        hidden_sizes=[32] * 3,\n",
    "                                                        output_size=3,\n",
    "                                                        activations=\"swish\"),\n",
    "                                    config_decoder=dict(input_size=3, hidden_sizes=[],\n",
    "                                                        output_size=2, activations=\"linear\"),\n",
    "                                    batch_size=1000, lbfgs=True,\n",
    "                                    normalize=True, normalize_mode='equal',\n",
    "                                    normalize_std_factor=1.0,\n",
    "                                    trainer_kwargs=dict(max_epochs=10))\n",
    "\n",
    "# train the vanilla NN model\n",
    "model = pk.Koopman(regressor=dlk_regressor)\n",
    "model.fit(traj_list, dt=nonlinear_sys.dt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "- If you didn't find the model to converge, that's because LBFGS is not very robust.\n",
    "You can try again and hope it works again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try NNDMD on training data but now we enforce the $A$ to be disspative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: GPU available: True (cuda), used: True\n",
      "[rank_zero.py:64 -                _info() ] GPU available: True (cuda), used: True\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "[rank_zero.py:64 -                _info() ] TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "[rank_zero.py:64 -                _info() ] IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "[rank_zero.py:64 -                _info() ] HPU available: False, using: 0 HPUs\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\trainer\\configuration_validator.py:69: UserWarning: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\n",
      "  rank_zero_warn(\"You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\")\n",
      "D:\\work\\pykoopman\\src\\pykoopman\\regression\\_nndmd.py:898: UserWarning: Warning: no validation data prepared\n",
      "  warn(\"Warning: no validation data prepared\")\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "[cuda.py:58 -     set_nvidia_flags() ] LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO: \n",
      "  | Name                | Type                       | Params\n",
      "-------------------------------------------------------------------\n",
      "0 | _encoder            | FFNN                       | 2.3 K \n",
      "1 | _decoder            | FFNN                       | 6     \n",
      "2 | _koopman_propagator | DissipativeKoopmanOperator | 12    \n",
      "3 | masked_loss_metric  | MaskedMSELoss              | 0     \n",
      "-------------------------------------------------------------------\n",
      "2.3 K     Trainable params\n",
      "0         Non-trainable params\n",
      "2.3 K     Total params\n",
      "0.009     Total estimated model params size (MB)\n",
      "[model_summary.py:83 -            summarize() ] \n",
      "  | Name                | Type                       | Params\n",
      "-------------------------------------------------------------------\n",
      "0 | _encoder            | FFNN                       | 2.3 K \n",
      "1 | _decoder            | FFNN                       | 6     \n",
      "2 | _koopman_propagator | DissipativeKoopmanOperator | 12    \n",
      "3 | masked_loss_metric  | MaskedMSELoss              | 0     \n",
      "-------------------------------------------------------------------\n",
      "2.3 K     Trainable params\n",
      "0         Non-trainable params\n",
      "2.3 K     Total params\n",
      "0.009     Total estimated model params size (MB)\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 20 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "D:\\work\\pykoopman\\venv\\lib\\site-packages\\lightning\\pytorch\\loops\\fit_loop.py:281: PossibleUserWarning: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "Training: 0it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "67a1507227704f01a13fef0e72e06c72"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "[rank_zero.py:64 -                _info() ] `Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={'activations': 'linear',\n                                        'hidden_sizes': [], 'input_size': 3,\n                                        'output_size': 2},\n                        config_encoder={'activations': 'swish',\n                                        'hidden_sizes': [32, 32, 32],\n                                        'input_size': 2, 'output_size': 3},\n                        dt=0.1, lbfgs=True, look_forward=20, mode='Dissipative',\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={'max_epochs': 10}))",
      "text/html": "<style>#sk-container-id-31 {color: black;background-color: white;}#sk-container-id-31 pre{padding: 0;}#sk-container-id-31 div.sk-toggleable {background-color: white;}#sk-container-id-31 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-31 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-31 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-31 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-31 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-31 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-31 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-31 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-31 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-31 div.sk-item {position: relative;z-index: 1;}#sk-container-id-31 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-31 div.sk-item::before, #sk-container-id-31 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-31 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-31 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-31 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-31 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-31 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-31 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-31 div.sk-label-container {text-align: center;}#sk-container-id-31 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-31 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-31\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [], &#x27;input_size&#x27;: 3,\n                                        &#x27;output_size&#x27;: 2},\n                        config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                                        &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n                        dt=0.1, lbfgs=True, look_forward=20, mode=&#x27;Dissipative&#x27;,\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={&#x27;max_epochs&#x27;: 10}))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-151\" type=\"checkbox\" ><label for=\"sk-estimator-id-151\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Koopman</label><div class=\"sk-toggleable__content\"><pre>Koopman(observables=Identity(),\n        regressor=NNDMD(batch_size=1000,\n                        config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [], &#x27;input_size&#x27;: 3,\n                                        &#x27;output_size&#x27;: 2},\n                        config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;,\n                                        &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                                        &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n                        dt=0.1, lbfgs=True, look_forward=20, mode=&#x27;Dissipative&#x27;,\n                        normalize_std_factor=1.0,\n                        trainer_kwargs={&#x27;max_epochs&#x27;: 10}))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-152\" type=\"checkbox\" ><label for=\"sk-estimator-id-152\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">observables: Identity</label><div class=\"sk-toggleable__content\"><pre>Identity()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-153\" type=\"checkbox\" ><label for=\"sk-estimator-id-153\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Identity</label><div class=\"sk-toggleable__content\"><pre>Identity()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-154\" type=\"checkbox\" ><label for=\"sk-estimator-id-154\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">regressor: NNDMD</label><div class=\"sk-toggleable__content\"><pre>NNDMD(batch_size=1000,\n      config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;, &#x27;hidden_sizes&#x27;: [],\n                      &#x27;input_size&#x27;: 3, &#x27;output_size&#x27;: 2},\n      config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;, &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                      &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n      dt=0.1, lbfgs=True, look_forward=20, mode=&#x27;Dissipative&#x27;,\n      normalize_std_factor=1.0, trainer_kwargs={&#x27;max_epochs&#x27;: 10})</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-155\" type=\"checkbox\" ><label for=\"sk-estimator-id-155\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NNDMD</label><div class=\"sk-toggleable__content\"><pre>NNDMD(batch_size=1000,\n      config_decoder={&#x27;activations&#x27;: &#x27;linear&#x27;, &#x27;hidden_sizes&#x27;: [],\n                      &#x27;input_size&#x27;: 3, &#x27;output_size&#x27;: 2},\n      config_encoder={&#x27;activations&#x27;: &#x27;swish&#x27;, &#x27;hidden_sizes&#x27;: [32, 32, 32],\n                      &#x27;input_size&#x27;: 2, &#x27;output_size&#x27;: 3},\n      dt=0.1, lbfgs=True, look_forward=20, mode=&#x27;Dissipative&#x27;,\n      normalize_std_factor=1.0, trainer_kwargs={&#x27;max_epochs&#x27;: 10})</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlk_regressor_d = pk.regression.NNDMD(mode='Dissipative',\n",
    "                                      dt=nonlinear_sys.dt,\n",
    "                                      look_forward=look_forward,\n",
    "                                    config_encoder=dict(input_size=2,\n",
    "                                                        hidden_sizes=[32] * 3,\n",
    "                                                        output_size=3,\n",
    "                                                        activations=\"swish\"),\n",
    "                                    config_decoder=dict(input_size=3, hidden_sizes=[],\n",
    "                                                        output_size=2, activations=\"linear\"),\n",
    "                                    batch_size=1000, lbfgs=True,\n",
    "                                    normalize=True, normalize_mode='equal',\n",
    "                                    normalize_std_factor=1.0,\n",
    "                                    trainer_kwargs=dict(max_epochs=10))\n",
    "\n",
    "# train the vanilla NN model\n",
    "model_d = pk.Koopman(regressor=dlk_regressor_d)\n",
    "model_d.fit(traj_list, dt=nonlinear_sys.dt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the continuous eigenvalues\n",
    "\n",
    "The eigenvalues identified is pretty close... to the ground truth. Although the\n",
    "dissipative one is less accurate, it guarantees stability, which can be very\n",
    "important for uncertainty quantification, and long time stability predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "koopman eigenvalues = [-0.99603385 -0.10004931 -0.20210291]\n",
      "koopman eigenvalues with dissipative enforcement = [-0.9720626  -0.09937927 -0.21690993]\n"
     ]
    }
   ],
   "source": [
    "koop_eigenvals = np.log(np.linalg.eig(model.A)[0])/nonlinear_sys.dt\n",
    "print(f\"koopman eigenvalues = {koop_eigenvals}\")\n",
    "\n",
    "koop_eigenvals = np.log(np.linalg.eig(model_d.A)[0])/nonlinear_sys.dt\n",
    "print(f\"koopman eigenvalues with dissipative enforcement = {koop_eigenvals}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model performance on test trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.4801627 , -0.06952379, -0.07016307],\n       [ 0.0335696 , -2.7245688 ,  0.4008407 ]], dtype=float32)"
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_d.ur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(-4.0, 4.0)"
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "Xkoop_edmd = model_edmd.simulate(x0, n_steps=len(t)-1)\n",
    "Xkoop_edmd = np.vstack([x0[np.newaxis,:], Xkoop_edmd])\n",
    "\n",
    "Xkoop_nn_d = model_d.simulate(x0, n_steps=len(t)-1)\n",
    "Xkoop_nn_d = np.vstack([x0[np.newaxis,:], Xkoop_nn_d])\n",
    "\n",
    "Xkoop_nn = model.simulate(x0, n_steps=len(t)-1)\n",
    "Xkoop_nn = np.vstack([x0[np.newaxis,:], Xkoop_nn])\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, tight_layout=True, figsize=(6, 6))\n",
    "axs[0].plot(t, Xtest[:, 0], '-', color='k', label='True')\n",
    "axs[0].plot(t, Xkoop_edmd[:, 0], '--r', label='EDMD')\n",
    "axs[0].plot(t, Xkoop_nn[:, 0], '--g', label='NNDMD')\n",
    "axs[0].plot(t, Xkoop_nn_d[:, 0], '--b', label='NNDMD-dissipative')\n",
    "\n",
    "axs[0].set(ylabel=r'$x_1$')\n",
    "axs[1].plot(t, Xtest[:, 1], '-', color='k', label='True')\n",
    "axs[1].plot(t, Xkoop_edmd[:, 1], '--r', label='EDMD')\n",
    "axs[1].plot(t, Xkoop_nn[:, 1], '--g', label='NNDMD')\n",
    "axs[1].plot(t, Xkoop_nn_d[:, 1], '--b', label='NNDMD-dissipative')\n",
    "axs[1].set(ylabel=r'$x_2$', xlabel=r'$t$')\n",
    "axs[1].legend(loc='best')\n",
    "\n",
    "axs[1].set_ylim([-4,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate linearity of identified Koopman eigenfunctions\n",
    "\n",
    "Koopman eigenfunctions evolve linearly in time and must satisfy:\n",
    "\n",
    "$$\\phi(x(t)) = \\phi(x(0)) \\exp(\\lambda t)$$\n",
    "\n",
    "It is possible to evaluate this expression on a test trajectory $x(t)$ with a\n",
    "linearity error defined by:\n",
    "\n",
    "$$e = \\Vert \\phi(x(t)) - \\phi(x(0)) \\exp(\\lambda t) \\Vert$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking of eigenfunctions by linearity error:  [4 7 6 8 3 5 1 0 2 9]\n",
      "Corresponding linearity error:  [1.787183161285686e-12, 3.60789557348859e-12, 1.0079736747933206e-11, 1.016677184845744e-11, 1.9276821336498066e-11, 2.0840686600530602e-11, 25.40265614296923, 25.402656142969267, 25.594030653971245, 110.7878547958471]\n"
     ]
    }
   ],
   "source": [
    "efun_index, linearity_error = model_edmd.validity_check(t, Xtest)\n",
    "print(\"Ranking of eigenfunctions by linearity error: \", efun_index)\n",
    "print(\"Corresponding linearity error: \", linearity_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking of eigenfunctions by linearity error:  [1 0 2]\n",
      "Corresponding linearity error:  [0.08088682341571321, 0.08260710527483629, 0.18168451100968588]\n"
     ]
    }
   ],
   "source": [
    "efun_index, linearity_error = model.validity_check(t, Xtest)\n",
    "print(\"Ranking of eigenfunctions by linearity error: \", efun_index)\n",
    "print(\"Corresponding linearity error: \", linearity_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking of eigenfunctions by linearity error:  [0 1 2]\n",
      "Corresponding linearity error:  [0.29899081987665693, 0.38791952389950124, 2.0498793587778]\n"
     ]
    }
   ],
   "source": [
    "efun_index, linearity_error = model_d.validity_check(t, Xtest)\n",
    "print(\"Ranking of eigenfunctions by linearity error: \", efun_index)\n",
    "print(\"Corresponding linearity error: \", linearity_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "You can see NN-DMD with one step optimizatin is not giving you the theoretical result. Although such model seems to fit the trajectory very well.\n",
    "\n",
    "Further, in below, you can see the EDMD eigenfunction can be huge because we are simply using monomials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization of eigenfunction on test trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[legend.py:1217 -   _parse_legend_args() ] No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "[legend.py:1217 -   _parse_legend_args() ] No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "[legend.py:1217 -   _parse_legend_args() ] No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 400x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 400x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 400x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "phi_edmd_test = model_edmd.psi(Xtest.T)\n",
    "phi_nn_test = model.psi(Xtest.T)\n",
    "phi_nn_d_test = model_d.psi(Xtest.T)\n",
    "    \n",
    "title_list = ['edmd','nndmd','nndmd-dissipative']\n",
    "phi_list = [phi_edmd_test, phi_nn_test, phi_nn_d_test]\n",
    "\n",
    "for i in range(3):\n",
    "    phi = phi_list[i]\n",
    "    fig = plt.figure(figsize=(4,4))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.plot(np.real(phi)[0,:],np.real(phi)[1,:],np.real(phi)[2,:])\n",
    "    ax.set_xlabel(r'$\\psi_0(x(t))$')\n",
    "    ax.set_ylabel(r'$\\psi_1(x(t))$')\n",
    "    ax.set_zlabel(r'$\\psi_2(x(t))$')\n",
    "    \n",
    "    plt.title(title_list[i])\n",
    "    plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access the matrices related to Koopman operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(3, 3)"
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 3)"
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.C.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 3)"
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.W.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
